SentiNets - ACM Web Science 2014 Pecha Kucha Presentation

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Full poster abstract at: http://dl.acm.org/citation.cfm?id=2615667

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SentiNets - ACM Web Science 2014 Pecha Kucha Presentation

  1. 1. “Just watched cyberbully-- it's annoying. Why would she kill herself? It's not worth it. Life is shit so deal with it :P” coded as negative “All the best to the retired players suffering from CTE. Spread the word so we can make the game safer.” coded as positive “New LGBT Research Study on same sex weddings [link]” coded as positive
  2. 2. Enthusiastic / Non-Supportive (E-NS) Enthusiastic / Supportive (E-S) Passive/ Non- Supportive (P-NS) Passive/ Supportive (P-S) Enthusiastic (E) Passive (P) Non-Supportive (NS) Supportive (S)
  3. 3. Create SentiNets Dashboard User Rankings User Networks Word Clouds Geo-location Scores Test Prediction on new data Legalize Marijuana Legalize Prostitution Train Classifier Enthusiastic/Passive Supportive/Non-Supportive Annotate Tweets using Codebook Enthusiastic/Passive Supportive/Non-Supportive Build Codebook Collect Tweets CTE in NFL Cyberbullying LGBT
  4. 4. Category Inter Coder Reliability Accuracy (SVM) Enthusiastic v/s Passive 93 % 79.0749 % Supportive v/s Non - Supportive 85 % 76.652 % 1500 Coded Tweets Refined Codebook for Social Causes Features used in classifier # of Emoticons # of URLS # of Mentions # of Hashtags Word Features # of Double Quotes Length of Tweets
  5. 5. “Just watched cyberbully-- it's annoying. Why would she kill herself? It's not worth it. Life is shit so deal with it :P” coded as Enthusiastic & Non- Supportive “All the best to the retired players suffering from CTE. Spread the word so we can make the game safer.” coded as Enthusiastic & Supportive “New LGBT Research Study on same sex weddings [link]” coded as Passive& Supportive
  6. 6. Confusion Matrices for sentiment classes for Legalize Marijuana and Legalize Prostitution
  7. 7. Node Color: HashTags or User Node Size: Occurrence Label Size: Sentiment Measure Support Sentiment based Networks for Global Warming Enthusiasm
  8. 8. Label Type Weight Tweet Count EP Count SNS Count EP_Class SNS_Class Degree Top in Class Sorted by Weights damnitstrue USER 91 0 -91 -91 PASSIVE NON_SUPPORTIVE 92 slone USER 51 0 -51 49 PASSIVE SUPPORTIVE 54 SUPPORTIVE tcot HASHTAG 50 0 -48 44 PASSIVE SUPPORTIVE 56 SUPPORTIVE Sorted by Number of Tweets ElectedMob USER 9 9 -9 1 PASSIVE NON_SUPPORTIVE 2 thomasj174 31826 USER 7 6 5 -5 ENTHUSIASTIC SUPPORTIVE 11 ENTHUSIASTIC NotCMBurns USER 6 6 2 6 ENTHUSIASTIC SUPPORTIVE 4 ENTHUSIASTIC User Rankings in Sentiment based Networks for Global Warming
  9. 9. http://context.lis.illinois.edu
  10. 10. SENTINETS Enthusiasm and Support: Alternative Sentiment Classification for Social Movements on Social Media Shubhanshu Mishra, Sneha Agarwal, Jinlong Guo, Kirstin Phelps, Johna Picco, Jana Diesner { smishra8, sagarwa8, jguo24, kphelps, picco2, jdiesner }@illinois.edu iSchool at University of Illinois at Urbana-Champaign More details at: http://people.lis.illinois.edu/~smishra8/sentinets.php

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